{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Tutorial on how to create an autoencoder w/ Tensorflow.\\n\\nParag K. Mital, Jan 2016\\n'"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"Tutorial on how to create an autoencoder w/ Tensorflow.\n",
    "\n",
    "Parag K. Mital, Jan 2016\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# %% Imports\n",
    "%matplotlib inline\n",
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "import math\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# %% Autoencoder definition\n",
    "def autoencoder(dimensions=[784, 512, 256, 64]):\n",
    "    \"\"\"Build a deep autoencoder w/ tied weights.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    dimensions : list, optional\n",
    "        The number of neurons for each layer of the autoencoder.\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    x : Tensor\n",
    "        Input placeholder to the network\n",
    "    z : Tensor\n",
    "        Inner-most latent representation\n",
    "    y : Tensor\n",
    "        Output reconstruction of the input\n",
    "    cost : Tensor\n",
    "        Overall cost to use for training\n",
    "    \"\"\"\n",
    "    # %% input to the network\n",
    "    x = tf.placeholder(tf.float32, [None, dimensions[0]], name='x')\n",
    "    current_input = x\n",
    "\n",
    "    # %% Build the encoder\n",
    "    encoder = []\n",
    "    for layer_i, n_output in enumerate(dimensions[1:]):\n",
    "        n_input = int(current_input.get_shape()[1])\n",
    "        W = tf.Variable(\n",
    "            tf.random_uniform([n_input, n_output],\n",
    "                              -1.0 / math.sqrt(n_input),\n",
    "                              1.0 / math.sqrt(n_input)))\n",
    "        b = tf.Variable(tf.zeros([n_output]))\n",
    "        encoder.append(W)\n",
    "        output = tf.nn.tanh(tf.matmul(current_input, W) + b)\n",
    "        current_input = output\n",
    "\n",
    "    # %% latent representation\n",
    "    z = current_input\n",
    "    encoder.reverse()\n",
    "\n",
    "    # %% Build the decoder using the same weights\n",
    "    for layer_i, n_output in enumerate(dimensions[:-1][::-1]):\n",
    "        W = tf.transpose(encoder[layer_i])\n",
    "        b = tf.Variable(tf.zeros([n_output]))\n",
    "        output = tf.nn.tanh(tf.matmul(current_input, W) + b)\n",
    "        current_input = output\n",
    "\n",
    "    # %% now have the reconstruction through the network\n",
    "    y = current_input\n",
    "\n",
    "    # %% cost function measures pixel-wise difference\n",
    "    cost = tf.reduce_sum(tf.square(y - x))\n",
    "    return {'x': x, 'z': z, 'y': y, 'cost': cost}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# %% Basic test\n",
    "def test_mnist():\n",
    "    \"\"\"Test the autoencoder using MNIST.\"\"\"\n",
    "    import tensorflow as tf\n",
    "    import tensorflow.examples.tutorials.mnist.input_data as input_data\n",
    "    # from matplotlib import use\n",
    "    # use('Agg')\n",
    "    import matplotlib.pyplot as plt\n",
    "\n",
    "    # %%\n",
    "    # load MNIST as before\n",
    "    mnist = input_data.read_data_sets('MNIST_data', one_hot=True)\n",
    "    mean_img = np.mean(mnist.train.images, axis=0)\n",
    "    ae = autoencoder(dimensions=[784, 256, 64])\n",
    "\n",
    "    # %%\n",
    "    learning_rate = 0.001\n",
    "    optimizer = tf.train.AdamOptimizer(learning_rate).minimize(ae['cost'])\n",
    "\n",
    "    # %%\n",
    "    # We create a session to use the graph\n",
    "    sess = tf.Session()\n",
    "    sess.run(tf.initialize_all_variables())\n",
    "\n",
    "    # %%\n",
    "    # Fit all training data\n",
    "    batch_size = 50\n",
    "    n_epochs = 10\n",
    "    for epoch_i in range(n_epochs):\n",
    "        for batch_i in range(mnist.train.num_examples // batch_size):\n",
    "            batch_xs, _ = mnist.train.next_batch(batch_size)\n",
    "            train = np.array([img - mean_img for img in batch_xs])\n",
    "            sess.run(optimizer, feed_dict={ae['x']: train})\n",
    "        print(epoch_i, sess.run(ae['cost'], feed_dict={ae['x']: train}))\n",
    "\n",
    "    # %%\n",
    "    # Plot example reconstructions\n",
    "    n_examples = 15\n",
    "    test_xs, _ = mnist.test.next_batch(n_examples)\n",
    "    test_xs_norm = np.array([img - mean_img for img in test_xs])\n",
    "    recon = sess.run(ae['y'], feed_dict={ae['x']: test_xs_norm})\n",
    "    fig, axs = plt.subplots(2, n_examples, figsize=(10, 2))\n",
    "    for example_i in range(n_examples):\n",
    "        axs[0][example_i].imshow(\n",
    "            np.reshape(test_xs[example_i, :], (28, 28)))\n",
    "        axs[1][example_i].imshow(\n",
    "            np.reshape([recon[example_i, :] + mean_img], (28, 28)))\n",
    "    fig.show()\n",
    "    plt.draw()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting MNIST_data/train-images-idx3-ubyte.gz\n",
      "Extracting MNIST_data/train-labels-idx1-ubyte.gz\n",
      "Extracting MNIST_data/t10k-images-idx3-ubyte.gz\n",
      "Extracting MNIST_data/t10k-labels-idx1-ubyte.gz\n",
      "(0, 554.17175)\n",
      "(1, 451.15411)\n",
      "(2, 420.77158)\n",
      "(3, 437.03281)\n",
      "(4, 409.98288)\n",
      "(5, 363.77893)\n",
      "(6, 420.08453)\n",
      "(7, 396.18784)\n",
      "(8, 365.31839)\n",
      "(9, 407.51379)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/heythisischo/anaconda2/lib/python2.7/site-packages/matplotlib/figure.py:397: UserWarning: matplotlib is currently using a non-GUI backend, so cannot show the figure\n",
      "  \"matplotlib is currently using a non-GUI backend, \"\n"
     ]
    },
    {
     "data": {
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PFYJMBNGM4+27EkKnUve+FnMiaL1hn88M3hUeafWy1KjenPL0B8Bkg6Sq1UCi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\n/w+Br7FGfQ8VhQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5d144a9fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# %%\n",
    "if __name__ == '__main__':\n",
    "    test_mnist()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [Root]",
   "language": "python",
   "name": "Python [Root]"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
